
'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase :   Tuple			    =	logging.get_logger(__name__)

_UpperCAmelCase :   List[str]			    =	{
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class       UpperCAmelCase					(       lowerCamelCase__			):





		"""simple docstring"""





		A__	: int    =     'sew'
		def __init__(     self	,       _snake_case=32	,       _snake_case=768	,       _snake_case=12	,       _snake_case=12	,       _snake_case=3072	,       _snake_case=2	,       _snake_case="gelu"	,       _snake_case=0.1	,       _snake_case=0.1	,       _snake_case=0.1	,       _snake_case=0.0	,       _snake_case=0.1	,       _snake_case=0.1	,       _snake_case=0.02	,       _snake_case=1E-5	,       _snake_case="group"	,       _snake_case="gelu"	,       _snake_case=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)	,       _snake_case=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)	,       _snake_case=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)	,       _snake_case=False	,       _snake_case=128	,       _snake_case=16	,       _snake_case=True	,       _snake_case=0.05	,       _snake_case=10	,       _snake_case=2	,       _snake_case=0.0	,       _snake_case=10	,       _snake_case=0	,       _snake_case="mean"	,       _snake_case=False	,       _snake_case=False	,       _snake_case=256	,       _snake_case=0	,       _snake_case=1	,       _snake_case=2	,       **_snake_case	,       )					->     Union[str, Any]:
				super().__init__(**__snake_case	,       pad_token_id=__snake_case	,       bos_token_id=__snake_case	,       eos_token_id=__snake_case  )
				_UpperCamelCase							:			Any     =   hidden_size
				_UpperCamelCase							:			Optional[Any]     =   feat_extract_norm
				_UpperCamelCase							:			int     =   feat_extract_activation
				_UpperCamelCase							:			List[str]     =   list(__snake_case  )
				_UpperCamelCase							:			Dict     =   list(__snake_case  )
				_UpperCamelCase							:			str     =   list(__snake_case  )
				_UpperCamelCase							:			Tuple     =   conv_bias
				_UpperCamelCase							:			int     =   num_conv_pos_embeddings
				_UpperCamelCase							:			int     =   num_conv_pos_embedding_groups
				_UpperCamelCase							:			str     =   len(self.conv_dim  )
				_UpperCamelCase							:			Tuple     =   num_hidden_layers
				_UpperCamelCase							:			Tuple     =   intermediate_size
				_UpperCamelCase							:			Dict     =   squeeze_factor
				_UpperCamelCase							:			Optional[int]     =   hidden_act
				_UpperCamelCase							:			Optional[Any]     =   num_attention_heads
				_UpperCamelCase							:			List[Any]     =   hidden_dropout
				_UpperCamelCase							:			Union[str, Any]     =   attention_dropout
				_UpperCamelCase							:			str     =   activation_dropout
				_UpperCamelCase							:			Dict     =   feat_proj_dropout
				_UpperCamelCase							:			str     =   final_dropout
				_UpperCamelCase							:			List[str]     =   layerdrop
				_UpperCamelCase							:			List[str]     =   layer_norm_eps
				_UpperCamelCase							:			List[Any]     =   initializer_range
				_UpperCamelCase							:			Optional[int]     =   vocab_size

				if (
				    (len(self.conv_stride  ) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel  ) != self.num_feat_extract_layers)
				    or (len(self.conv_dim  ) != self.num_feat_extract_layers)
				):
						raise ValueError(
						    '''Configuration for convolutional layers is incorrect.'''
						    '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
						    F'''but is `len(config.conv_dim) = {len(self.conv_dim  )}`, `len(config.conv_stride)'''
						    F'''= {len(self.conv_stride  )}`, `len(config.conv_kernel) = {len(self.conv_kernel  )}`.'''  )

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				_UpperCamelCase							:			List[Any]     =   apply_spec_augment
				_UpperCamelCase							:			int     =   mask_time_prob
				_UpperCamelCase							:			str     =   mask_time_length
				_UpperCamelCase							:			str     =   mask_time_min_masks
				_UpperCamelCase							:			Union[str, Any]     =   mask_feature_prob
				_UpperCamelCase							:			int     =   mask_feature_length
				_UpperCamelCase							:			Any     =   mask_feature_min_masks

				# ctc loss
				_UpperCamelCase							:			Union[str, Any]     =   ctc_loss_reduction
				_UpperCamelCase							:			List[Any]     =   ctc_zero_infinity

				# sequence classification
				_UpperCamelCase							:			Dict     =   use_weighted_layer_sum
				_UpperCamelCase							:			Optional[Any]     =   classifier_proj_size




		@property
		def _lowercase	(     self  )					->     Any:
				return functools.reduce(operator.mul	,       self.conv_stride	,       1  )










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase:   List[Any]					=		{'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Dict					=		['ReformerTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Tuple					=		['ReformerTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ReformerAttention',
         'ReformerForMaskedLM',
         'ReformerForQuestionAnswering',
         'ReformerForSequenceClassification',
         'ReformerLayer',
         'ReformerModel',
         'ReformerModelWithLMHead',
         'ReformerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_reformer import ReformerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_reformer_fast import ReformerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_reformer import (
              REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              ReformerAttention,
              ReformerForMaskedLM,
              ReformerForQuestionAnswering,
              ReformerForSequenceClassification,
              ReformerLayer,
              ReformerModel,
              ReformerModelWithLMHead,
              ReformerPreTrainedModel,
          )

else:
     import sys

     lowerCAmelCase:   str					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)